package com.yupi.yuaiagent.rag;

import jakarta.annotation.Resource;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.pgvector.PgVectorStore;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.jdbc.DataSourceBuilder;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Lazy;
import org.springframework.context.annotation.Primary;
import org.springframework.jdbc.core.JdbcTemplate;

import javax.sql.DataSource;
import java.util.List;

import static org.springframework.ai.vectorstore.pgvector.PgVectorStore.PgDistanceType.COSINE_DISTANCE;
import static org.springframework.ai.vectorstore.pgvector.PgVectorStore.PgIndexType.HNSW;

//@Configuration
public class PgVectorVectorStoreConfig {

    @Resource
    private LoveAppDocumentLoader loveAppDocumentLoader;

    @Bean
    public DataSource postgresDataSource(@Value("${pg-datasource.url}") String url,
                                         @Value("${pg-datasource.username}") String username,
                                         @Value("${pg-datasource.password}") String password) {
        return DataSourceBuilder.create()
                .url(url)
                .username(username)
                .password(password)
                .build();
    }

    @Bean
    public JdbcTemplate pgJdbcTemplate(DataSource postgresDataSource) {
        return new JdbcTemplate(postgresDataSource);
    }

    @Bean
    public VectorStore pgVectorVectorStore(JdbcTemplate pgJdbcTemplate, EmbeddingModel dashscopeEmbeddingModel) {
        VectorStore vectorStore = PgVectorStore.builder(pgJdbcTemplate, dashscopeEmbeddingModel)
                .dimensions(1536)                    // Optional: defaults to model dimensions or 1536
                .distanceType(COSINE_DISTANCE)       // Optional: defaults to COSINE_DISTANCE
                .indexType(HNSW)                     // Optional: defaults to HNSW
                .initializeSchema(true)              // Optional: defaults to false
                .schemaName("public")                // Optional: defaults to "public"
                .vectorTableName("vector_store")     // Optional: defaults to "vector_store"
                .maxDocumentBatchSize(10000)         // Optional: defaults to 10000
                .build();
        // 加载文档
//        List<Document> documents = loveAppDocumentLoader.loadMarkdowns();
//        vectorStore.add(documents);
        return vectorStore;
    }
}

